README.md

IntelliAnswer

Goal:

A system that processes user-provided question files and supplementary documents. It extracts questions, answers them using information from the supplementary files when available, and falls back to an LLM for answers when necessary.

Installation

1. Create a virtual environment (optional but recommended)

   python -m venv llmrag

2. Install all the dependencies

   pip install -r requirements.txt

If you want to use Ollama with local models then follow this steps:

3. Download Ollama from here [https://ollama.com/download]

4. Run ollama after installing

5. In terminal you need to pull the llama and nomic-embed-text. Although you can use any of the model available in the ollama repository.

   ollama run llama3
   ollama pull nomic-embed-text

6. Verify your installation

   ollama list

7. Now run the python file. For instance, you can use the following command to run the langchain_ollama_llama3_rag_for_docx.py script.

   python3 langchain_ollama_llama3_rag_for_docx.py

If you want to run the webapp, you can use the following command. Make sure you have the OPENAI_API_KEY set to your .env file

streamlit run QEAG_webapp.py

Note:

• Before running the script, you must specify the filepath in the main function.

• If your docx file is large enough, then try to tweak the chunk_size and chunk_overlap parameters accordingly.

  def split_documents(documents):
      text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=1000)
      chunks = text_splitter.split_documents(documents)
      document = chunks[0]
      print(document.page_content)
      print(document.metadata)
      print(f"Split into {len(chunks)} chunks")
      return chunks